In medical imaging, information being collected is typically reduced to a few measured parameters subsequent to state of the art image processing. These measured parameters may for example include subcutaneous fat volume, visceral fat volume, muscle volume, tumor diameter, and liver fat content. Information from imaging comprising millions of measurement samples is often reduced to a few output parameter values.
Imaging applications are typically designed to provide answers to questions concerning one or more a priori determined target parameters, for example fat volume or tumor diameter. This is typically achieved by gross filtering of image information resulting in a large data reduction.
Traditional whole body image analysis approaches provide a few measures from images analyzed separately.
In neuroimaging, statistical parameter mapping (SPM) and voxel based morphometry (VBM) are concepts for processing of magnetic resonance imaging (MRI) image data of brain only, (Ref. 1). By using image registration using a common standardized volume and performing segmentation of different tissues, such as grey matter and white matter, group comparisons and correlation analysis can be performed by statistics on morphological or functional data. The registration methods used herein are not stable enough to be suitable for whole body images.
In radiomics, a feature analysis of pre-segmented regions can be made (Ref. 2). This is an initiative to use radiology medical imaging to monitor the development and progression of cancer or its response to therapy providing a comprehensive quantification of a tumor phenotype. Radiomics enables high-throughput extraction of a large amount of quantitative features from radiology medical images of a given modality, such as computed tomography (CT), positron emission tomography (PET), and MR, and can provide complementary and interchangeable information compared to sources such as demographics, pathology, blood biomarkers, or genomics, improving individualized treatment selection and monitoring. The statistical analysis is radiomics is restricted to pre-defined, pre-segmented regions of the images, only.
Gupta et al. (Ref. 3) presented a method for building a statistical whole-body atlas using pre-segmented objects, landmarks, by generative or discriminative learning methods. However, the statistical analysis on non-imaging (meta) data, voxel intensities, statistics on global and local shape deformations or statistics on joint articulations is limited to analyzing one volume at the time of the atlas. However, such atlases are not based on fat and/or water MR images.
Moreover, a statistical atlas on texture features as well as a method for voxel-wise multivariate analysis thereof, is known from Poole (Ref. 4), focusing on brain images. By determining a measure of the difference between image data and the statistical atlas, the presence of abnormality can be determined. Analysis of a whole body is mentioned, but stated to be ‘too difficult to achieve’. The statistical analyzes enabled seem to be limited to comparisons of one image with many other images.
Also, multi-band registration of water-fat MRI images has been used for segmentation of abdominal fat from water-fat MRI images (Ref. 5). A multi-band method that utilizes signal from both fat and water images is used to register the abdominal water-fat MRI images. This abdominal MRI images are obviously restricted to the abdomen, which is a disadvantage.
In addition, a method for registration of whole-body water-fat MRI images has been applied to segmentation and quantification of abdominal fat and to muscle quantification (Ref. 6). This registration method is unavoidably sensitive to noise and acquisition artefacts, as it does not utilize pre-computed features. Image registration herein is limited to one band, being water image only. Since one band is used only tissue specific features are used herein. Although comparisons between images are performed (multi-atlas approach), implicitly comprising some adaptation of an image, such adaptation is not explicitly taken advantage of in studies presented. A statistical analysis is carried out on pre-defined regions only.
In the published US patent application US 2014/0270446 A1, a method and apparatus for registration of multimodal imaging data using constraints are disclosed. The object image is segmented into one or more anatomic segments, being identified organs or tissues, associated with an anatomic class. A registration is performed constrained by assigned attributes.
A survey of medical image registration techniques is presented by J. B. A. Maintz and M. A. Viergever in “A survey of medical image registration”, in Medical Image Analysis (1998) vol. 2, No. 1, pp. 1-36.
The methods as described above are either focused on too small a region of interest, or use a limited number of bands or images, which restricts the resolution and the accuracy of any possible analyzes of registered images.
There is therefore a need for an improved image registering method, by which at least some of the issues outlined above are addressed.